The incidence of esophageal adenocarcinoma (EA) in the US has increased 300% over recent decades while median survival remains an abysmal 10 months. Several major risk factors have been defined for EA and its precursor, Barrett's esophagus (BE), including reflux, Caucasian race, male gender, obesity, and smoking, but a key challenge remains the identification of individuals at highest risk, since most with reflux do not develop BE, and most with BE do not progress to EA. While a number of genetic, epigenetic, and protein-based biomarkers have been evaluated for their ability to detect or predict disease progression, the development of novel clinical tests has been hampered by technical complexities of the required assays, limited sensitivity/specificity of the marker panels and lack of validation studies. Based on our preliminary data, we anticipate that metabolomics, the quantification of low molecular weight metabolites, offers a favorable alternative approach for identifying effective biomarkers to aid in risk stratification for those at risk of EA. Numerou studies have established that cellular energy metabolism is fundamentally altered in a broad spectrum of cancers, and increasing evidence indicates that cancer-associated metabolic changes are detectable in blood and urine. In this proposal, we hypothesize that serum-based metabolites can distinguish multiple stages of disease progression along the pathway from gastroesophageal reflux (GERD) to BE to EA. Using advanced profiling methods and a well-characterized biorepository, this collaborative proposal seeks to identify and validate novel metabolite markers linked to these disease states. In Aim 1, global metabolic profiling will be conducted on a set of 60 patient serum samples distributed across case type (GERD, BE, high- grade dysplasia/EA) to identify an expanded pool of candidate metabolite biomarkers that differ significantly across two or all of these conditions. In Aim 2, this targeted profiling of this set f metabolites plus a previously-identified panel of 28 candidates will be conducted on a larger set of 240 patient samples. Based on validated candidate markers identified, statistical models for sample classification (BE-GERD and HGD/EA-BE) will be constructed using partial least-squares discriminant analysis, and model performance estimated by Monte Carlo cross-validation. Successful completion of these aims will lay the groundwork for generating novel non-invasive tools for stratifying patients at risk of EA according to their likelihood of disease progression. Such tools have the potential to increase early detection and reduce unnecessary surveillance, by focusing intensive clinical monitoring on those at highest risk of cancer. These investigations will set the stage for larger-scale future studies, which will leverage the extensiv resources of the international Barrett's and Esophageal Adenocarcinoma (BEACON) consortium to validate our findings across population groups and identify key modifying factors that may enhance the accuracy and applicability of our predictive models.